code: def prepare(args):
      bs, idim, odim, ilens, olens = get_default_scope_inputs()
      n_token = odim - 1
  
      model = E2E(idim, odim, args)
  
      feats = torch.randn(bs, max(ilens), idim)
      labels = (torch.rand(bs, max(olens)) * n_token % n_token).long()
  
      for i in range(bs):
          feats[i, ilens[i] :] = -1
          labels[i, olens[i] :] = model.ignore_id
  
      data = {}
      uttid_list = []
      for i in range(bs):
          data["utt%d" % i] = {
              "input": [{"shape": [ilens[i], idim]}],
              "output": [{"shape": [olens[i]]}],
          }
          uttid_list.append("utt%d" % i)
  
      return model, feats, torch.tensor(ilens), labels, data, uttid_list
  
imports: import argparse
  import json
  import tempfile
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
label_desc: Use image node_id 6 for calling a global function with example usage: prepare(args) and returns: model,  feats,  torch,  labels,  data,  uttid_list